Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Background: China launched a new round of healthcare-system reform in 2009 and proposed the goal of equal and guaranteed essential medical and health services for all by 2020. We aimed to investigate the changes in China’s health resources over the past ten years after the healthcare reform.Methods: Data were collected from the China Statistical Yearbook and China Health Statistics Yearbook from 2009 to 2018. Four categories and ten indicators of health resources were analyzed. A descriptive analysis was used to present the overall condition. The Health Resource Density Index was applied to showcase health-resource distribution in demographic and geographic dimensions. The global and local Moran’s I were used to assess the spatial autocorrelation of health resources. Concentration Index (CI) was used to quantify the equity of health-resource distribution. A Geo-Detector model and Geographic Weighted Regression (GWR) were applied to assess the association between gross domestic product (GDP) per capita and health resources.Results: Health resources have increased over the past ten years. The global and local Moran’s I suggested spatial aggregation in the distribution of health resources. Hospital beds were concentrated in wealthier areas, but this inequity decreased yearly (from CI=0.0587 in 2009 to CI=0.0021 in 2018). Primary medical and health institutions (PMHI) and their beds were concentrated in poorer areas (CI remained negative). Healthcare employees were concentrated in wealthier areas (CI remained positive). In 2017, the q-statistics indicated that the explanatory power of GDP per capita to beds, health personnel, and health expenditure was 40.7%, 50.3%, and 42.5%, respectively. The coefficients of GWR remained positive with statistical significance, indicating the positive association between GDP per capita and health resources.Conclusions: From 2009 to 2018, the total amount of health resources in China has increased substantially. Spatial aggregation existed in the health-resources distribution. Health resources tended to be concentrated in wealthier areas. When allocating health resources, the governments should take economic factors into account.

Background: Internet hospitals in China are being rapidly developed as an innovative approach to providing health services. The ongoing COVID-19 pandemic has triggered the development of internet hospitals that promote outpatient service delivery to the public via internet technologies. To date, no studies have assessed China's internet hospitals during the COVID-19 pandemic. Objective: This study aimed to elucidate the characteristics of China's internet hospitals and assess the health service capacity of these hospitals. Methods: Data on 711 internet hospitals were collected from official websites, the WeChat (Tencent Inc) platform, smartphone apps, and the Baidu search engine until July 16, 2020. Results: As of July 16, 2020, 711 internet hospitals were developed in mainland China. More than half of these internet hospitals (421/711, 59.2%) were established during 2019 (206/711, 29%) and 2020 (215/711, 30.2%). Furthermore, about one-third (215/711, 30.2%) of internet hospitals were established at the beginning of 2020 as an emergency response to the COVID-19 epidemic. The 711 internet hospitals consisted of the following 3 types of hospitals: government-oriented (42/711, 5.91%), hospital-oriented (143/711, 20.11%), and enterprise-oriented internet hospitals (526/711, 73.98%). The vast majority of internet hospitals were traditional hospitals (526/711, 74%). Nearly 46.1% (221/711) of internet hospitals requested doctors to provide health services at a specific web clinic. Most patients (224/639, 35.1%) accessed outpatient services via WeChat. Internet hospitals’ consulting methods included SMS text messaging consultations involving the use of graphics (552/570, 96.8%), video consultations (248/570, 43.5%), and telephone consultations (238/570, 41.8%). The median number of available web-based doctors was 43, and the median consultation fees of fever clinics and other outpatient clinics were ¥0 (US $0) per consultation and ¥6 (US $0.93) per consultation, respectively. Internet hospitals have provided various services during the COVID-19 pandemic, including medical prescription, drug delivery, and medical insurance services. Conclusions: The dramatic increase of internet hospitals in China has played an important role in the prevention and control of COVID-19. Internet hospitals provide different and convenient medical services for people in need.

Household solid-fuel burning contributes to indoor air pollution and is linked to poor cognitive function, but how solid cooking fuel use leads to cognitive decline over time is not well elaborated. We examine the associations of solid cooking fuel with cognitive function among three nationally representative cohorts. This study uses data from the 2010-2018 China Family Panel Studies (CFPS), the 2011-2018 China Health and Retirement Longitudinal Study (CHARLS) and the 2003-2015 Mexican Health and Aging Study (MHAS) in adults over the age of 50. Time varying Cox model was conducted to measure the association between cooking fuel types and cognitive decline. Mediation analysis was used to estimate the potential mediation effects on the associations of cooking fuel types with cognitive decline risk. Respondents in CFPS, CHARLS, and MHAS relied on solid cooking fuel at baseline approximately 56 %, 51 %, and 12 %, respectively. Using solid fuel was consistently associated with higher risk of cognitive decline in three cohorts (CFPS: HR = 1.300 [95 % CI: 1.201, 1.407], CHARLS: HR = 1.179 [95 % CI: 1.059, 1.312], MHAS: HR = 1.237 [95 % CI: 1.123, 1.362]). Compared to those with persistent solid fuel, persistent clean fuel and change from solid fuel to clean fuel were associated with a lower risk of cognitive decline. Hypertension, diabetes, physical activity, dyslipidemia and high-density lipoprotein cholesterol (HDL-C) may partially mediate the cognitive decline caused by solid fuel use. Of the cognitive decline burden, 18.23 % (95 % CI: 12.21 %, 24.73 %) in CFPS, 8.90 % (95 % CI: 2.93 %, 15.52 %) in CHARLS and 2.92 % (95 % CI: 1.52 %, 4.46 %) in MHAS of cognitive decline cases attributable to solid cooking fuel use. The use of solid cooking fuel is associated with a higher risk of cognitive decline. It is essential to promote the expanded use of clean fuel to protect cognitive health.